import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates
import plotly.express as px
import plotly.graph_objects as go
import re
from datetime import datetime, timedelta
import warnings
import time
import dask.dataframe as dd
state_to_region = {
    # WEST
    'AK': 'WEST', 'CA': 'WEST', 'CO': 'WEST', 'HI': 'WEST', 'ID': 'WEST', 
    'MT': 'WEST', 'NV': 'WEST', 'OR': 'WEST', 'UT': 'WEST', 'WA': 'WEST', 'WY': 'WEST',
    
    # SOUTHWEST
    'AZ': 'SOUTHWEST', 'NM': 'SOUTHWEST', 'OK': 'SOUTHWEST', 'TX': 'SOUTHWEST',
    
    # MIDWEST
    'IL': 'MIDWEST', 'IN': 'MIDWEST', 'IA': 'MIDWEST', 'KS': 'MIDWEST', 'MI': 'MIDWEST',
    'MN': 'MIDWEST', 'MO': 'MIDWEST', 'NE': 'MIDWEST', 'ND': 'MIDWEST', 'OH': 'MIDWEST', 
    'SD': 'MIDWEST', 'WI': 'MIDWEST',
    
    # SOUTHEAST
    'AL': 'SOUTHEAST', 'AR': 'SOUTHEAST', 'DE': 'SOUTHEAST', 'FL': 'SOUTHEAST', 
    'GA': 'SOUTHEAST', 'KY': 'SOUTHEAST', 'LA': 'SOUTHEAST', 'MD': 'SOUTHEAST', 
    'MS': 'SOUTHEAST', 'NC': 'SOUTHEAST', 'SC': 'SOUTHEAST', 'TN': 'SOUTHEAST', 
    'VA': 'SOUTHEAST', 'WV': 'SOUTHEAST',
    
    # NORTHEAST
    'CT': 'NORTHEAST', 'ME': 'NORTHEAST', 'MA': 'NORTHEAST', 'NH': 'NORTHEAST', 
    'NJ': 'NORTHEAST', 'NY': 'NORTHEAST', 'PA': 'NORTHEAST', 'RI': 'NORTHEAST', 
    'VT': 'NORTHEAST'
}
@st.cache_data
def date_from_week(year, week):
    # Assuming the fiscal year starts in August and the week starts from August 1st
    base_date = pd.to_datetime((year - 1).astype(str) + '-08-01')
    dates = base_date + pd.to_timedelta((week - 1) * 7, unit='days')
    return dates

@st.cache_data
def load_data(active_card):
    # st.write(f"{active_card}")
    # Define columns common to multiple cards if there are any
    common_cols = ['FyWeek', 'Itemtype', 'Chaincode', 'State', 'SalesVolume', 'UnitPrice', 'Sales']

    # Columns specific to cards
    card_specific_cols = {
        'card1': ['FyWeek', 'Fy', 'State','Store','Address','Zipcode','City','Itemtype', 'Chaincode', 'Containercode', 'SalesVolume', 'UnitPrice', 'Sales'],
        # 'card2': ['FyWeek', 'Fy', 'State','Store','Address','Zipcode','City','Itemtype', 'Chaincode', 'Containercode', 'SalesVolume', 'UnitPrice', 'Sales'],
        'card3': ['FyWeek', 'Fy', 'State','Store','Address','Zipcode','City','Itemtype', 'Chaincode', 'Containercode', 'SalesVolume', 'UnitPrice', 'Sales'] # Added for PE calculation card
    }

    # Choose columns based on the active card
    required_columns = card_specific_cols.get(active_card, common_cols)

    # Define the data types for efficient memory usage
    dtype_spec = {
        'FyWeek': 'string',
        'Fy': 'category',  # Add data type for 'Fy' if it's used
        'Itemtype': 'category',
        'Chaincode': 'category',
        'State': 'category',
        "Store": "category", 
        'Containercode': 'category',
        "Address": "string",
        "Zipcode": "float",
        "City": "category", 
        'SalesVolume': 'float',
        'UnitPrice': 'float',
        'Sales': 'float'
    }

    # Read only the necessary columns
    # st.write(required_columns)
    ddf = dd.read_csv("fy21-24.csv", usecols=required_columns, dtype=dtype_spec)
    df = ddf.compute()

    # st.write("+++++++++++++++++++++++")

    if active_card in ['card1','card2', 'card3',]:
        df = df.groupby(['FyWeek', 'Fy', 'Chaincode', 'Store', 'Address', 'Zipcode', 'City', 'State', 'Containercode', 'Itemtype'], observed=True).agg({
        'SalesVolume': 'sum',
        'UnitPrice': 'mean',
        'Sales': 'sum'
        }).reset_index()
        df[['FY', 'Week']] = df['FyWeek'].str.split(' Week ', expand=True)
        df['Week'] = df['Week'].astype(int)  # Convert 'Week' to int
        df['Year'] = df['FY'].str[2:].astype(int)  # Extract year part and convert to int
        df['Dt'] = date_from_week(df['Year'], df['Week'])
    # Add the region column based on state
    df['Region'] = df['State'].map(state_to_region)

    return df
    
# Display logo
st.image("bonnie.png", width=150)  # Adjust width as needed

# Display title
# st.title("Price vs. Sales Volume Tracker Dashboard")


#  Initialize session state for storing which card was clicked and item type
if 'active_card' not in st.session_state:
    st.session_state['active_card'] = None
if 'selected_item_type' not in st.session_state:
    st.session_state['selected_item_type'] = 'CORE'  # Set default to 'CORE'

if 'selected_feature' not in st.session_state:
    st.session_state['selected_feature'] = 'Chaincode'  # Default to 'Chain Code'

# Card selection buttons with logic to reset session state on switch
col1, col3 = st.columns(2)
with col1:
    if st.button("Sales Volume Trend"):
        st.session_state['active_card'] = 'card1'
        # Reset other selections when switching cards
        st.session_state['selected_state'] = None
        st.session_state['selected_chaincode'] = None
        st.session_state['selected_itemtype'] = None
        st.session_state['selected_containercode'] = None

# with col2:
#     if st.button("Sales Volume vs Median Unit Price Trend"):
#         st.session_state['active_card'] = 'card2'
#         # Reset selections when switching cards
#         st.session_state['selected_state'] = None
#         st.session_state['selected_chaincode'] = None
#         st.session_state['selected_itemtype'] = None
#         st.session_state['selected_containercode'] = None

with col3:
    if st.button("Price Elasticity Coefficient Trend YoY"):
        st.session_state['active_card'] = 'card3'
        # Reset selections when switching cards
        st.session_state['selected_state'] = None
        st.session_state['selected_chaincode'] = None
        st.session_state['selected_itemtype'] = None
        st.session_state['selected_containercode'] = None

# Load data for the current card
start_time = time.time()
df = load_data(st.session_state['active_card'])
time_taken = time.time() - start_time
st.write(f"Data loaded in {time_taken:.2f} seconds")


############################################ CARD #1 ####################################################
if st.session_state['active_card'] == 'card1':
    # Step 1: Sales Volume vs FyWeek for the whole dataset (no filter)
    st.subheader("Total Sales Volume by Fiscal Week")
    df['FY_Week'] = df['FY'].astype(str) + '_' + df['Week'].astype(str)
    # Split FY_Week again for correct sorting
    if not df.empty and 'FY_Week' in df.columns:
        total_sales_df = df.groupby('FY_Week', observed=True)['SalesVolume'].sum().reset_index()
        total_sales_df[['FY', 'Week']] = total_sales_df['FY_Week'].str.split('_', expand=True)
        total_sales_df['Week'] = total_sales_df['Week'].astype(int)
        total_sales_df = total_sales_df.sort_values(by=['FY', 'Week'])
        
        # Create a line chart using Plotly
        fig = px.line(total_sales_df, x='FY_Week', y='SalesVolume',
                      labels={'SalesVolume': 'Sales Volume', 'FY_Week': 'Fiscal Week'})
        st.plotly_chart(fig)

    # Step 2: Top 3 states based on sales volume as buttons/cards
    top_states = df.groupby('State', observed=True)['SalesVolume'].sum().nlargest(3).index
    st.write("### Top 3 Selling States in the last 4 years (drill down by state)")
    col1, col2, col3 = st.columns(3)
    if len(top_states) > 0 and col1.button(top_states[0]):
        st.session_state['selected_state'] = top_states[0]
    if len(top_states) > 1 and col2.button(top_states[1]):
        st.session_state['selected_state'] = top_states[1]
    if len(top_states) > 2 and col3.button(top_states[2]):
        st.session_state['selected_state'] = top_states[2]

    # If a state is selected, show the corresponding plot
    if 'selected_state' in st.session_state and st.session_state['selected_state']:
        selected_state = st.session_state['selected_state']

        # Step 3: Sales volume vs FyWeek for the selected state
        st.subheader(f"Sales Volume by Fiscal Week for {selected_state} (drill down by Chaincode) ")
        state_sales_df = df[df['State'] == selected_state].groupby('FY_Week', observed=True)['SalesVolume'].sum().reset_index()

        if not state_sales_df.empty and 'FY_Week' in state_sales_df.columns:
            state_sales_df[['FY', 'Week']] = state_sales_df['FY_Week'].str.split('_', expand=True)
            state_sales_df['Week'] = state_sales_df['Week'].astype(int)
            state_sales_df = state_sales_df.sort_values(by=['FY', 'Week'])

            fig = px.line(state_sales_df, x='FY_Week', y='SalesVolume',
                          labels={'SalesVolume': 'Sales Volume', 'FY_Week': 'Fiscal Week'})
            st.plotly_chart(fig)

        # Step 4: Top 3 chaincodes based on sales volume as buttons/cards
        top_chaincodes = df[df['State'] == selected_state].groupby('Chaincode', observed=True)['SalesVolume'].sum().nlargest(3).index
        st.write(f"### Top 3 selling Chaincode in {selected_state}:")

        # Add a check to ensure top_chaincodes has values before accessing
        col1, col2, col3 = st.columns(3)
        if len(top_chaincodes) > 0 and col1.button(top_chaincodes[0]):
            st.session_state['selected_chaincode'] = top_chaincodes[0]
        if len(top_chaincodes) > 1 and col2.button(top_chaincodes[1]):
            st.session_state['selected_chaincode'] = top_chaincodes[1]
        if len(top_chaincodes) > 2 and col3.button(top_chaincodes[2]):
            st.session_state['selected_chaincode'] = top_chaincodes[2]

        # If a chaincode is selected, show the corresponding plot
        if 'selected_chaincode' in st.session_state:
            selected_chaincode = st.session_state['selected_chaincode']

            # Step 5: Sales volume vs FyWeek for the selected chaincode in the selected state
            st.subheader(f"Sales Volume by Fiscal Week for {selected_chaincode} in {selected_state}")
            chain_sales_df = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode)].groupby('FY_Week', observed=True)['SalesVolume'].sum().reset_index()

            if not chain_sales_df.empty and 'FY_Week' in chain_sales_df.columns:
                chain_sales_df[['FY', 'Week']] = chain_sales_df['FY_Week'].str.split('_', expand=True)
                chain_sales_df['Week'] = chain_sales_df['Week'].astype(int)
                chain_sales_df = chain_sales_df.sort_values(by=['FY', 'Week'])

                fig = px.line(chain_sales_df, x='FY_Week', y='SalesVolume',
                              # title=f'Sales Volume vs Fiscal Week in {selected_chaincode}, {selected_state}',
                              labels={'SalesVolume': 'Sales Volume', 'FY_Week': 'Fiscal Week'})
                st.plotly_chart(fig)

            # Step 6: Top 3 itemtypes based on sales volume as buttons/cards
            top_itemtypes = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode)].groupby('Itemtype', observed=True)['SalesVolume'].sum().nlargest(3).index
            st.write(f"### Top Item Type in {selected_chaincode}, {selected_state} (drill down by ItemType) :")

            col1, col2, col3 = st.columns(3)
            if len(top_itemtypes) > 0 and col1.button(top_itemtypes[0]):
                st.session_state['selected_itemtype'] = top_itemtypes[0]
            if len(top_itemtypes) > 1 and col2.button(top_itemtypes[1]):
                st.session_state['selected_itemtype'] = top_itemtypes[1]
            if len(top_itemtypes) > 2 and col3.button(top_itemtypes[2]):
                st.session_state['selected_itemtype'] = top_itemtypes[2]

            # If an itemtype is selected, show the corresponding dual-axis plot for Sales Volume & Unit Price
            if 'selected_itemtype' in st.session_state:
                selected_itemtype = st.session_state['selected_itemtype']

                # Step 7: Dual-axis plot for Sales volume and UnitPrice vs FyWeek for the selected itemtype
                # st.subheader(f"Sales Volume & Unit Price vs Fiscal Week for {selected_itemtype} in {selected_chaincode}, {selected_state}")
                item_sales_df = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode) & (df['Itemtype'] == selected_itemtype)].groupby('FY_Week', observed=True).agg({
                    'SalesVolume': 'sum',
                    'UnitPrice': 'mean'
                }).reset_index()
                if not item_sales_df.empty and 'FY_Week' in item_sales_df.columns:
                    item_sales_df[['FY', 'Week']] = item_sales_df['FY_Week'].str.split('_', expand=True)
                    item_sales_df['Week'] = item_sales_df['Week'].astype(int)
                    item_sales_df = item_sales_df.sort_values(by=['FY', 'Week'])

                    # Dual-axis plot using Plotly Graph Objects
                    fig = go.Figure()

                    # Add SalesVolume trace
                    fig.add_trace(go.Scatter(
                        x=item_sales_df['FY_Week'],
                        y=item_sales_df['SalesVolume'],
                        mode='lines+markers',
                        name='SalesVolume',
                        line=dict(color='blue'),
                        hovertemplate='SalesVolume: %{y}<br>Week-Year: %{x}'
                    ))

                    # Add UnitPrice trace with secondary Y-axis
                    fig.add_trace(go.Scatter(
                        x=item_sales_df['FY_Week'],
                        y=item_sales_df['UnitPrice'],
                        mode='lines+markers',
                        name='UnitPrice',
                        line=dict(color='green'),
                        yaxis='y2',
                        hovertemplate='UnitPrice: %{y}<br>Week-Year: %{x}'
                    ))

                    # Update layout for dual axes
                    fig.update_layout(
                        title=f"Sales Volume vs Unit Price by Fiscal Week for {selected_itemtype}, {selected_chaincode}, {selected_state}",
                        xaxis_title='Fiscal Week',
                        yaxis_title='Sales Volume',
                        yaxis2=dict(title='Unit Price', overlaying='y', side='right'),
                        legend=dict(x=0.9, y=1.15),
                        hovermode="x unified",  # Show both values in a tooltip
                        height=600,
                        margin=dict(l=50, r=50, t=50, b=50)
                    )

                    # Rotate X-axis labels
                    fig.update_xaxes(tickangle=90)

                    # Display the Plotly figure in Streamlit
                    st.plotly_chart(fig, use_container_width=True)
                    # Step 8: Display Top/Bottom Container Codes and Stores
                    st.subheader("Top & Bottom 3 Container Codes and Stores")

                    # Get top and bottom 3 container codes based on SalesVolume
                    top_containercodes = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode) & (df['Itemtype'] == selected_itemtype)].groupby('Containercode', observed=True)['SalesVolume'].sum().nlargest(3).reset_index()
                    bottom_containercodes = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode) & (df['Itemtype'] == selected_itemtype)].groupby('Containercode', observed=True)['SalesVolume'].sum().nsmallest(3).reset_index()

                    # Get top and bottom 3 stores based on SalesVolume
                    top_stores = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode) & (df['Itemtype'] == selected_itemtype)].groupby('Store', observed=True)['SalesVolume'].sum().nlargest(3).reset_index()
                    bottom_stores = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode) & (df['Itemtype'] == selected_itemtype)].groupby('Store', observed=True)['SalesVolume'].sum().nsmallest(3).reset_index()

                    # Display top and bottom container codes side by side
                    st.write("### Container Codes:")
                    col1, col2 = st.columns(2)
                    with col1:
                        st.write("#### Top 3 Container Codes")
                        st.dataframe(top_containercodes)
                    with col2:
                        st.write("#### Bottom 3 Container Codes")
                        st.dataframe(bottom_containercodes)

                    # Display top and bottom stores side by side
                    st.write("### Stores:")
                    col3, col4 = st.columns(2)
                    with col3:
                        st.write("#### Top 3 Stores")
                        st.dataframe(top_stores)
                    with col4:
                        st.write("#### Bottom 3 Stores")
                        st.dataframe(bottom_stores)
##########################################################################################################


########################################### CARD #2 ####################################################
# if st.session_state['active_card'] == 'card2':
#     # Identify the top 10 Itemtypes based on total SalesVolume
#     top_10_itemtypes = df.groupby('Itemtype')['SalesVolume'].sum().nlargest(10).index

#     # Filter the DataFrame to include only the top 10 Itemtypes
#     df = df[df['Itemtype'].isin(top_10_itemtypes)]
#     # Dropdown to select item type (using session_state)
#     st.session_state['selected_item_type'] = st.selectbox(
#         'Select Item Type', df['Itemtype'].unique(),
#         index=list(df['Itemtype'].unique()).index(st.session_state['selected_item_type']))

#     # Dropdown to select the grouping category (container code, chain code, or state)
#     group_by_option = st.selectbox('Group by', ['Containercode', 'Chaincode', 'State','Region'])

#     # Multi-select checkbox to select multiple years
#     selected_years = st.multiselect('Select Year(s)', [2021, 2022, 2023, 2024], default=[2021])

#     st.subheader(f"Sales Volume & Unit Price Correlation for {group_by_option} in {', '.join(map(str, selected_years))}")

#     # Convert 'Dt' column to datetime
#     df['Dt'] = pd.to_datetime(df['Dt'], errors='coerce')
#     df['Promo'] = np.where(df['Dt'].dt.month.astype(str).isin(['3', '4', '5', '6']), 'Promo', 'NoPromo')
#     df["Promo"] = df["Promo"].astype("category")

#     # Filter the dataframe based on the selected item type and selected years
#     filtered_df = df[(df['Itemtype'] == st.session_state['selected_item_type']) & (df['Dt'].dt.year.isin(selected_years))]

#     # Find the top 3 values based on total SalesVolume in the selected grouping category
#     top_3_values = filtered_df.groupby(group_by_option, observed=True)['SalesVolume'].sum().nlargest(3).index

#     # Filter the data for only the top 3 values
#     top_group_data = filtered_df[filtered_df[group_by_option].isin(top_3_values)]
    
#     # Aggregate data
#     agg_df = top_group_data.groupby([group_by_option, 'Year', 'Week', 'Dt'], observed=True).agg({
#         'SalesVolume': 'sum',
#         'UnitPrice': 'mean'
#     }).reset_index()

#     # Create a new column 'week-year' for X-axis labels
#     agg_df['week-year'] = agg_df['Dt'].dt.strftime('%U-%Y')

#     # Loop through the top 3 values and create separate plots using Plotly
#     for value in top_3_values:
#         value_data = agg_df[agg_df[group_by_option] == value]
#         # Assuming you have 'value_data' from your previous code
#         mean_sales_volume = value_data['SalesVolume'].mean()
#         mean_unit_price = value_data['UnitPrice'].mean()

#         # Create a Plotly figure
#         fig = go.Figure()

#         # Add SalesVolume trace
#         fig.add_trace(go.Scatter(
#             x=value_data['week-year'],
#             y=value_data['SalesVolume'],
#             mode='lines+markers',
#             name='SalesVolume',
#             line=dict(color='blue'),
#             hovertemplate='SalesVolume: %{y}<br>Week-Year: %{x}'
#         ))

#         # Add UnitPrice trace on a secondary Y-axis
#         fig.add_trace(go.Scatter(
#             x=value_data['week-year'],
#             y=value_data['UnitPrice'],
#             mode='lines+markers',
#             name='UnitPrice',
#             line=dict(color='green'),
#             yaxis='y2',
#             hovertemplate='UnitPrice: %{y}<br>Week-Year: %{x}'
#         ))
#         # Add mean line for SalesVolume
#         fig.add_shape(type="line",
#                     x0=value_data['week-year'].min(), x1=value_data['week-year'].max(),
#                     y0=mean_sales_volume, y1=mean_sales_volume,
#                     line=dict(color="blue", width=2, dash="dash"),
#                     xref='x', yref='y')

#         # Add mean line for UnitPrice (on secondary Y-axis)
#         fig.add_shape(type="line",
#                     x0=value_data['week-year'].min(), x1=value_data['week-year'].max(),
#                     y0=mean_unit_price, y1=mean_unit_price,
#                     line=dict(color="green", width=2, dash="dash"),
#                     xref='x', yref='y2')

#         # Update layout for dual axes
#         fig.update_layout(
#             template='plotly_white',
#             title=f"SalesVolume and UnitPrice - {value} ({group_by_option})",
#             xaxis_title='Week-Year',
#             yaxis_title='Sales Volume',
#             yaxis2=dict(title='UnitPrice', overlaying='y', side='right'),
#             legend=dict(x=0.9, y=1.15),
#             hovermode="x unified",  # Show both values in a tooltip
#             height=600,
#             margin=dict(l=50, r=50, t=50, b=50)
#         )

#         # Rotate X-axis labels
#         fig.update_xaxes(tickangle=90)

#         # Display the Plotly figure in Streamlit
#         st.plotly_chart(fig, use_container_width=True)

################################
if st.session_state['active_card'] == 'card3':
    # Dropdown for selecting the item type
    item_type_options = df['Itemtype'].unique()
    selected_item_type = st.selectbox("Select Item Type", item_type_options)

    # Dropdown for selecting the region (multiple selection allowed)
    region_options = df['Region'].dropna().unique()
    selected_regions = st.multiselect("Select Region(s)", region_options, default=region_options)

    # Filter data based on selected item type and selected regions
    filtered_df = df[(df['Itemtype'] == selected_item_type) & (df['Region'].isin(selected_regions))]

    # Group by Year, Region, Itemtype and Promo, and aggregate SalesVolume and UnitPrice
    agg_df = filtered_df.groupby(['Fy', 'Region', 'Itemtype',]).agg({
        'SalesVolume': 'sum',
        'UnitPrice': 'mean'
    }).reset_index()

    # Sort values by Region, Itemtype, Fy, and Promo for YOY calculation
    agg_df = agg_df.sort_values(by=['Region', 'Itemtype', 'Fy',])

    # Calculate YOY percentage changes in Sales Volume and Unit Price
    agg_df['SalesVolume_pct_change'] = agg_df.groupby(['Region', 'Itemtype',])['SalesVolume'].pct_change().round(3) * 100
    agg_df['UnitPrice_pct_change'] = agg_df.groupby(['Region', 'Itemtype', ])['UnitPrice'].pct_change().round(3) * 100

    # Calculate Price Elasticity Coefficient (PE)
    agg_df['PE_Coeff'] = (agg_df['SalesVolume_pct_change'] / agg_df['UnitPrice_pct_change']).round(2)

    # Exclude FY 2025 but keep FY 2021 even with NaN values
    agg_df_filtered = agg_df[agg_df['Fy'] != 'FY 2025']

    # Drop rows where PE_Coeff is NaN (optional)
    agg_df_filtered = agg_df_filtered.dropna(subset=['PE_Coeff'])
    agg_df_filtered = agg_df_filtered.rename(columns={
    'SalesVolume_pct_change': 'SlVol%change',
    'UnitPrice_pct_change': 'UnPr%change',
    })
    agg_df_filtered = agg_df_filtered.reset_index(drop=True)
    st.dataframe(agg_df_filtered)
    st.write(agg_df_filtered.shape)
    # Extract values for the current and previous years from row 1 and row 2 of the dataframe
    current_year_row = agg_df_filtered.iloc[1]  # Row 1 - Current Year
    previous_year_row = agg_df_filtered.iloc[0]  # Row 2 - Previous Year

    # Extract values for Unit Price and Sales Volume
    unit_price_current_year = current_year_row['UnitPrice']
    unit_price_previous_year = previous_year_row['UnitPrice']
    sales_volume_current_year = current_year_row['SalesVolume']
    sales_volume_previous_year = previous_year_row['SalesVolume']

    # Calculate percentage changes for Unit Price and Sales Volume
    unit_price_pct = ((unit_price_current_year - unit_price_previous_year) / unit_price_previous_year) * 100
    sales_volume_pct = ((sales_volume_current_year - sales_volume_previous_year) / sales_volume_previous_year) * 100

    # Calculate PE Coefficient
    pe_coeff = sales_volume_pct / unit_price_pct

    st.markdown(f'''### Calculations for Price Elasticity Coefficient''')
    st.latex(rf"""
    \text{{Unit Price \% Change}} = \frac{{{unit_price_current_year:.2f} - {unit_price_previous_year:.2f}}}{{{unit_price_previous_year:.2f}}} \times 100 = {unit_price_pct:.2f}\%
    """)

    # Sales Volume % Change
    st.latex(rf"""
    \text{{Sales Volume \% Change}} = \frac{{{sales_volume_current_year:.2f} - {sales_volume_previous_year:.2f}}}{{{sales_volume_previous_year:.2f}}} \times 100 = {sales_volume_pct:.2f}\%
    """)

    # PE Coefficient
    st.latex(rf"""
    \text{{PE Coefficient}} = \frac{{{sales_volume_pct:.2f}}}{{{unit_price_pct:.2f}}} = {pe_coeff:.2f}
    """)

    # Explanation for PE Coefficient Conditions
    st.markdown(f"""
    ### Interpretation of Price Elasticity (PE) Coefficient:
    The Price Elasticity (PE) coefficient reflects how sensitive sales volume is to changes in unit price.

    - If the **PE coefficient is positive**:
    1. When the price increases, sales volume increases.
    2. When the price decreases, sales volume decreases.
    
    - If the **PE coefficient is negative**:
    1. When the price increases, sales volume decreases.
    2. When the price decreases, sales volume increases.
    
    """)

    # Dynamic analysis based on the calculated PE coefficient and signs of changes
    if unit_price_pct > 0 and sales_volume_pct > 0:
        st.warning(f"""
        Both unit price and sales volume increased (refer first and second row of the table). The PE coefficient of **{pe_coeff:.2f}** indicates that for every 1% increase in unit price, sales volume increased by approximately **{pe_coeff:.2f}%**.
        """)
    elif unit_price_pct < 0 and sales_volume_pct < 0:
        st.warning(f"""
        Both unit price and sales volume decreased (refer first and second row of the table). The PE coefficient of **{pe_coeff:.2f}** suggests that for every 1% decrease in unit price, sales volume decreased by approximately **{pe_coeff:.2f}%**.
        """)
    elif unit_price_pct > 0 and sales_volume_pct < 0:
        st.warning(f"""
        The unit price increased while sales volume decreased (refer first and second row of the table). The negative PE coefficient of **{pe_coeff:.2f}** means that for every 1% increase in unit price, sales volume fell by approximately **{abs(pe_coeff):.2f}%**.
        """)
    elif unit_price_pct < 0 and sales_volume_pct > 0:
        st.warning(f"""
        The unit price decreased while sales volume increased (refer first and second row of the table). The negative PE coefficient of **{pe_coeff:.2f}** implies that for every 1% decrease in unit price, sales volume increased by approximately **{abs(pe_coeff):.2f}%**.
        """)
    # Plot the PE Coefficient with Plotly
    fig = px.line(
        agg_df_filtered, 
        x='Fy', 
        y='PE_Coeff',  # Differentiate between Promo and NoPromo
        color='Region',  # Differentiate lines by Region
        title=f"Price Elasticity Coefficient (PE) by Year for {selected_item_type}",
        labels={'Fy': 'Fiscal Year', 'PE_Coeff': 'Price Elasticity Coefficient'},
        markers=True
    )

    # Customize layout and show plot
    fig.update_layout(
        height=600,
        width=1000,
    )

    st.plotly_chart(fig, use_container_width=True)

    #################### CARD-3 MONTHLY IMPLEMENTATION #########################
    # Ensure 'Dt' column is in datetime format
    df['Dt'] = pd.to_datetime(df['Dt'])

    # Extract fiscal year and month from 'Dt' column
    df['FY'] = df['Dt'].dt.year.astype(str)
    df['Month'] = df['Dt'].dt.month.astype(str)

    # Create FY_Month column
    df['FY_Month'] = df['FY'] + '_' + df['Month']

    # Filter data based on selected item type and selected regions
    filtered_df = df[(df['Itemtype'] == selected_item_type) & (df['Region'].isin(selected_regions))]

    # Group by Year, Region, Itemtype and aggregate SalesVolume and UnitPrice
    agg_df = filtered_df.groupby(['FY_Month', 'Region', 'Itemtype']).agg({
        'SalesVolume': 'sum',
        'UnitPrice': 'mean'
    }).reset_index()

    # Split FY_Month again for correct sorting
    agg_df[['FY', 'Month']] = agg_df['FY_Month'].str.split('_', expand=True)
    agg_df['Month'] = agg_df['Month'].astype(int)
    agg_df['FY'] = agg_df['FY'].astype(int)

    # Combine FY and Month back into a datetime-like format for proper sorting
    agg_df['FY_Month_dt'] = pd.to_datetime(agg_df['FY'].astype(str) + agg_df['Month'].astype(str).str.zfill(2), format='%Y%m')

    # Sort values by Region, Itemtype, and FY_Month_dt
    agg_df = agg_df.sort_values(by=['Region', 'Itemtype', 'FY_Month_dt'])

    # Calculate YOY percentage changes in Sales Volume and Unit Price
    agg_df['SalesVolume_pct_change'] = agg_df.groupby(['Region', 'Itemtype'])['SalesVolume'].pct_change().round(3) * 100
    agg_df['UnitPrice_pct_change'] = agg_df.groupby(['Region', 'Itemtype'])['UnitPrice'].pct_change().round(3) * 100

    # Calculate Price Elasticity Coefficient (PE)
    agg_df['PE_Coeff'] = (agg_df['SalesVolume_pct_change'] / agg_df['UnitPrice_pct_change']).round(2)

    # Exclude FY 2021 and FY 2025
    agg_df_filtered = agg_df[~agg_df['FY'].astype(str).str.contains('2020|2021|2025')]

    # Drop rows where PE_Coeff is NaN (optional)
    agg_df_filtered = agg_df_filtered.dropna(subset=['PE_Coeff'])
    agg_df_filtered = agg_df_filtered[(agg_df_filtered['PE_Coeff'] < 1000) & (agg_df_filtered['PE_Coeff'] > -1000)]

    # Plot the PE Coefficient with Plotly
    fig = go.Figure()

    # Iterate through each selected region and plot separately
    for region in selected_regions:
        # Filter the DataFrame for the current region
        region_df = agg_df_filtered[agg_df_filtered['Region'] == region]
        
        # Add a line trace for the region
        fig.add_trace(go.Scatter(
            x=region_df['FY_Month_dt'],  # Use the datetime-like column for correct sorting
            y=region_df['PE_Coeff'], 
            mode='lines+markers',
            name=region,  # Set the name to the region to appear in the legend
            line=dict(width=2),
            marker=dict(size=6),
        ))

    # Customize layout
    fig.update_layout(
        title=f"Price Elasticity Coefficient (PE) by Year-Month for {selected_item_type}",
        xaxis_title="Fiscal Year_Month",
        yaxis_title="Price Elasticity Coefficient (PE)",
        height=600,
        width=1000,
        legend_title="Region",
        xaxis=dict(
            tickformat='%Y-%m',  # Format X-axis ticks as Year-Month
        )
    )

    # Show the plot in Streamlit
    st.plotly_chart(fig, use_container_width=True)